Method and apparatus for providing equipment maintenance via a network

ABSTRACT

A method and apparatus for providing equipment maintenance are disclosed. For example, the method receives data that is captured by at least one sensor deployed at a location, wherein the data is associated with at least one parameter, receives atmospheric data for the location, monitors the at least one parameter of the equipment and the atmospheric data for the location, determines for the at least one parameter, whether an update to a risk measure associated with the equipment is needed, when a deviation from a baseline is detected that is greater than a threshold for a maximum deviation from the baseline, performs the update to the risk measure associated with the equipment, when the update to the risk measure associated with the equipment is needed, and generates a ticket for a remedial action based on the risk measure, when the risk measure triggers the scheduling of the remedial action.

The present disclosure relates to a method and apparatus for providingequipment maintenance via a communications network, e.g., acommunications network of a network service provider.

BACKGROUND

An enterprise may have a large number of equipment deployed in variouslocations, e.g., various buildings. Each building may contain any numberof equipment, e.g., Heating, Ventilation, and Air Conditioning (HVAC)units, humidifiers, etc. HVAC units are equipment used for providingcontrol of temperature and indoor air quality. Typically, buildingoperations personnel select settings for an indoor temperature, ahumidity level, an amount of ventilation, etc., via a controller. Whenthe HVAC unit is working properly, the actual temperature and quality ofindoor air should closely correlate with the environmental parametersset by the building operations personnel. When the HVAC unit fails,there may be inadequate ventilation, heating, or air conditioning.Consequently, the quality of the indoor environment may suffer. As such,the equipment, e.g., HVACs, deployed in the various locations mayrequire scheduled maintenance for proper operation.

SUMMARY OF THE DISCLOSURE

In one embodiment, the present disclosure teaches a method and apparatusfor providing equipment maintenance via a communications network. Forexample, the method receives data that is captured by at least onesensor deployed at a location, wherein the data is associated with atleast one parameter of an equipment, wherein the equipment is deployedat the location, receives atmospheric data for the location, monitorsthe at least one parameter of the equipment and the atmospheric data forthe location, determines for the at least one parameter of theequipment, whether an update to a risk measure associated with theequipment is needed, when a deviation from a baseline is detected thatis greater than a threshold for a maximum deviation from the baselinethat is established for the at least one parameter, performs the updateto the risk measure associated with the equipment, when the update tothe risk measure associated with the equipment is needed, and generatesa ticket for a remedial action based on the risk measure associated withthe equipment, when the risk measure associated with the equipmenttriggers the scheduling of the remedial action.

BRIEF DESCRIPTION OF THE DRAWINGS

The teaching of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example network related to the present disclosure;

FIG. 2 illustrates a flowchart of an example method for providingequipment maintenance; and

FIG. 3 depicts a high-level block diagram of a computer suitable for usein performing the functions described herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION

The present disclosure relates to a method and apparatus for providingequipment maintenance via a communications network, e.g., acommunications network of a network service provider. The teachings ofthe present disclosure may be applied via any type of wired or wirelesscommunications network.

As described above, scheduled maintenance can be provided for a largenumber of equipment, e.g., a large number of HVAC units, humidifiers,etc., at various equipment sites. One way for Managing the large numberof equipment is by performing a scheduled maintenance in a predeterminedinterval, e.g., annually, semi-annually, quarterly, monthly, etc. As aresult, ensuring the equipment are operating as expected will requirefrequent scheduled visits to the various equipment sites. For example,maintenance personnel will be periodically dispatched to each equipmentsite irrespective as to whether a problem actual exists at the equipmentsite. During some scheduled visits, the maintenance personnel mayidentify issues and perform repairs, whereas during some other visits,the maintenance personnel may find that all of the equipment arefunctioning as expected and no repair is required. This method ofsending maintenance personnel to offsite locations to perform scheduledmaintenance does improve reliability of the equipment but at great cost,e.g., performing preventive replacement or maintenance of equipment whenno failure is actually detected. A review of maintenance records over anumber of years may reveal that the frequent scheduled dispatches wereprobably unnecessary.

One way to reduce unnecessary dispatches of maintenance personnel is towait for the equipment to actually fail. For example, a circuit carddeployed in the HVAC system may have failed and the HVAC system can nolonger maintain a temperature set for a particular environment. Whenthis condition is determined by a tenant of a building who may reportthe failure, maintenance personnel may be sent to address the failure,e.g., replacing the failed circuit card. Although this approach solvesthe problem of unnecessary dispatches of maintenance personnel, it oftencreates significant inconvenience for the occupants of the building.

The present disclosure provides maintenance (i.e., equipmentmaintenance) for one or more equipment in a “just-in-time” approach. Foreach equipment, the equipment maintenance of the present disclosure isperformed in a timely manner, i.e., prior to an actual failure of themonitored equipment. For each equipment, the equipment maintenance isbased on a baseline that is generated for one or more parameters of theequipment.

In one embodiment, the baseline for a parameter of the one or moreparameters of the equipment is generated based on one or more measurableaspects of the equipment and atmospheric data for a location at whichthe equipment is deployed. The location comprises a geographicallocation (e.g., a physical location).

In one embodiment, each parameter of the one or more parameters of theequipment is a parameter that is defined for tracking at least onemeasurable aspect of the equipment. In one embodiment, each measurableaspect of the at least one measurable aspect is indicative of one ormore of: an efficiency of the equipment in performing at least one taskof the equipment, a likelihood of a potential failure of the equipmentto perform at least one task of the equipment, and a degradation of anability of the equipment in performing at least one task of theequipment.

For an illustrative example, suppose the equipment is an airconditioner. Then, the tasks of the air conditioner may comprise:cooling a room and reducing a humidity level of a room. A measurableaspect of the equipment may be for identifying a symptom of inefficiencyas to performing at least one of the tasks in the list of tasks of theair conditioner, a degradation of efficiency as to performing at leastone of the tasks in the list of tasks of the air conditioner, or alikelihood of a potential failure to perform at least one of the tasksin the list of tasks of the air conditioner. The parameter may then bedefined for tracking how efficiently the air conditioner is able toperform the task of cooling a room or reducing a humidity level of aroom.

For example, suppose an air conditioner operating optimally runs 25% ofthe time to cool a room to 78° F. when the outside temperature is 90° F.Suppose, as the air conditioner becomes less efficient, the airconditioner starts running for longer periods of time. For example, itruns 50% of the time to cool the same room to 78° F. when the outsidetemperature is 90° F. In this example, the percentage of time that theair conditioner is running may be an appropriate measurable aspect ofthe air conditioner for identifying a symptom of inefficiency as to theperforming of the task of air conditioning. Then, a parameter may bedefined for measuring and tracking the percentage of time the airconditioner is running. As the value of the parameter that is definedfor the percentage of time the air conditioner is running reaches apredetermined threshold, e.g., the air conditioner is running 50% of thetime, maintenance may be performed on the air conditioner prior to anactual failure of the air conditioner. This “just-in-time” approachsolves the problem of performing unnecessary preventive maintenanceoperations while avoiding actual failure of the equipment.

As described above, the equipment may have to perform any number oftasks. For example, an HVAC system may be used to perform the tasks of:air conditioning, heating, and air ventilating. Accordingly, aparticular parameter may be defined, for the equipment, for tracking andindicating how efficiently the equipment performs each particular task.For instance, for the HVAC system, a first parameter may be forindicating how well a condenser of the HVAC system is able to performthe task of air conditioning, a second parameter may be for indicatinghow well a heat pump of the HVAC system is able to perform the task ofheating, a third parameter may be for indicating how well a ventilationcomponent of the HVAC system is able to perform the task of keeping theindoor air quality in a desired range, and the like.

In one embodiment, the one or more measurable aspects of the equipmentare determined from various sources that comprise one or more of: amanufacturer of the equipment, a manufacturer of a component of theequipment, a subject matter expert, and an agency, e.g., a governmentagency. In one example, data for predicting a failure may be identifiedby a manufacturer of the equipment. For instance, the manufacturer mayidentify an increase in a coolant temperature of an HVAC system, anincrease in a humidity level measured near the HVAC system, and a carbondioxide level measured near the HVAC system, as appropriate measurableaspects for predicting a failure of the HVAC system. The measurableaspects of the equipment may then be the coolant temperature, thehumidity level near the HVAC system, and the carbon dioxide level nearthe HVAC system.

In another example, a government agency may be tasked with providingguidance and/or standards for an indoor air quality. For instance, inthe United States of America, the Environmental Protection Agency (EPA)sets standards for indoor air quality to ensure that biological,chemical and particulate levels in buildings are at levels that wouldnot impact the satisfaction, productivity and health of the occupants.The measurable aspects of the equipment in buildings may then be basedon the air quality standards set by the agency providing the guidanceand/or standards, e.g., the EPA. For an illustrative example, supposethe EPA sets, for an indoor space, a maximum of 9 ppm for an averagecarbon monoxide level over a 24 hour period. Then, the measurable aspectis the carbon monoxide level. One or more sensors may be used to takemeasurements of carbon monoxide levels in a building. The carbonmonoxide levels that are observed may be provided to a server in apredetermined time interval. For example, the carbon monoxide levels maybe provided to an application server of a network service provider,e.g., every 10 minutes, every hour, every four hours, every day, etc.The average of the carbon monoxide levels may then be computed for adesired period of time, e.g., a period of 24 hours. If the average ofthe carbon monoxide levels that is computed for the 24 hour periodexceeds the 9 ppm, an maintenance action may be taken. For example,maintenance personnel may be dispatched to identify a cause for theexcessive level of carbon monoxide in the building and/or perform aremedial action.

As described above, for each equipment of the one or more equipment, theone or more measurable aspects of the equipment are determined fromvarious sources. For each equipment at a location, once the one or moremeasurable aspects of the equipment are known, the enterprise may deployone or more sensors for measuring each of the one or more measurableaspects of the equipment. For example, the enterprise may have deploy atthe location one or more: thermometers for measuring temperature,hygrometers for measuring humidity, CO₂ meters for measuring carbondioxide levels, CO meters for measuring carbon monoxide levels, and thelike. A thermometer may be for capturing a temperature of a space (e.g.,room) or a coolant of the equipment itself. In one embodiment, theenterprise may also deploy one or more other sensors to capture energyconsumption, run times of each condenser, run times of each heat pump,volume of air flow per given time period, and the like. For illustrativeexample, suppose a building has two HVAC systems with a temperature of acoolant being identified as being a measurable aspect for each of thetwo HVAC systems. Then, for each of the two HVAC systems, the enterprisemay deploy a thermometer for sensing a temperature of the coolant of therespective HVAC system.

When deployment of one or more equipment and the one or more sensors iscompleted for the location, the network service provider is providedwith information about the one or more equipment and the one or moresensors that are deployed at the location. The information about the oneor more equipment and the one or more sensors at the location maycomprise one or more of: an address of a building, a latitudinal andlongitudinal coordinate of the location, an altitudinal location (ifapplicable), electricity rate for the location, a list of the one ormore equipment, a size and a capacity of each equipment, a number ofunits of each equipment, a lead unit designation (if applicable), a listof the one or more sensors, and information about each sensor.

In one embodiment, the information about a particular sensor of the oneor more sensors at the location may comprise one or more of: a type ofpower source used by the particular sensor, and a type of communicationused by the particular sensor for transmitting data captured by theparticular sensor. For example, the particular sensor may transmitcaptured data to a server, e.g., an application server of a networkservice provider, via an access network, e.g., via a cellular network ora Wi-Fi access point. In another example, the particular sensor maytransmit data captured by the particular sensor to an application serverof a network service provider via a dedicated local area network (LAN).In another example, the particular sensor may transmit data captured bythe particular sensor to an application server of a network serviceprovider via a local gateway server. The local gateway server can bedeployed in the vicinity of the particular sensor. For example, theparticular sensor and the local gateway server may be in a samebuilding.

In one embodiment, the network service provider or the enterprisedetermines a time frequency of capturing data via the one or moresensors. For example, the frequency of capturing data (i.e., performingthe sensing) may be every ten minutes, every hour, etc. Forillustration, a thermometer may capture a temperature of a coolant or aroom every ten minutes and provide the captured temperature (i.e.,temperature data) to a server, e.g., to a dedicated maintenanceapplication server of a network service provider. In one embodiment, thecaptured data is aggregated over a predetermined time period prior tobeing provided to the server, e.g., the captured data is stored locallyby the sensor for a predefined period of time to minimize the number ofcommunication sessions with the application server. For the exampleabove, the thermometer may capture the temperature of the coolant everyten minutes, and aggregate the temperature of the coolant that iscaptured for each day. At the end of each day, the temperature data thatis captured and aggregated for the day may be provided to the server.

Each particular sensor has at least one way to provide the data that iscaptured by the particular sensor to the network service provider. Inone embodiment, each particular sensor transmits the data captured bythe particular sensor directly to a server, e.g., an application server,located in the communications network of the network service provider.For example, a particular sensor may have an ability to communicate withthe server, e.g., the application server, via an access network, e.g., aWireless-Fidelity (Wi-Fi) network, a cellular network (e.g., 2G, 3G, andthe like), a long term evolution (LTE) network, and the like.

In one embodiment, each particular sensor of the one or more sensorstransmits the data captured by the particular sensor to a local gatewayserver. The local gateway server is physically located near the locationof the one or more sensors. For instance, the local gateway server andthe one or more sensors may be located in a same building, in a samefloor, or in a same room. The local gateway server gathers data from theone or more sensors and forwards the data that it gathered from the oneor more sensors to a server, e.g., to the application server located inthe communications network of the network service provider. For anillustrative example, if a building has five HVAC units and each HVACunit has ten (10) sensors, it may be beneficial to have each particularsensor of the fifty (50) sensors sending data captured by the particularsensor to a local gateway server in the building. The local gatewayserver may then provide the data that is gathered from any number of thefifty sensors to the application server located in the communicationsnetwork of the network service provider. This will again reduce thenumber of communication sessions with the application server of thenetwork service provider.

In one embodiment, the local gateway server provides the data that isgathered from any number of the one or more sensors to the applicationserver in a predetermined time interval. For example, the data that isgathered by the local gateway server may be provided to the applicationserver every four hours, every eight hours, every day, etc. In oneembodiment, the local gateway server provides the data that is gatheredto the application server upon receiving a query. For instance, theapplication server may send a query to the local gateway server whenpreparing to perform an analysis on data captured via the one or moresensors at the location. Thus, the local gateway server is capable ofproviding the data that is gathered to the application server in apredetermined time interval and/or upon receiving an on-demand query. Inother words, the local gateway server is responsive to queries from theapplication server in addition to being configured to provide the datathat is gathered in a predetermined time interval.

In some scenarios, there may be a need to have one or more sensorsdeployed at a location where a power line is not readily available.Moreover, a location may not be appropriate for a connection to a LocalArea Network (LAN). Thus, there may be a location where using sensorsthat require no power line or a connection to a LAN may be moredesirable. For such a location, sensors that operate on batteries may bedeployed. Then each particular sensor that operates on batteries and hasno connection to a LAN may transmit data captured by the particularsensor to the server, e.g., the application server of the networkservice provider, via a wireless access network, e.g., via a cellularnetwork or a W-Fi access point.

As described above, each particular sensor of the one or more sensorstransmits the data that is captured by the particular sensor to amaintenance server, e.g., an application server deployed in thecommunications network of the network service provider. The equipmentmaintenance is then provided via the maintenance server, e.g., theapplication server. In order to provide the equipment maintenance inaccordance to the teachings of the present disclosure, the applicationserver receives the data that is captured by each of the one or moresensors. The data that is captured by the one or more sensors isassociated with at least one parameter of one or more parameters of anequipment, wherein the equipment is deployed at the location.

In one embodiment, the operation of receiving the data that is capturedby a particular sensor of the one or more sensors is performed via alocal gateway server deployed at the location. In one embodiment, theoperation of receiving the data that is captured by a particular sensorof the one or more sensors is performed directly, where the receiving ofthe data that is captured directly comprises receiving the data that iscaptured without having the data that is captured traversing a localgateway server. For example, the data that is captured may be receivedfrom the particular sensor that captured the data via a wireless accessnetwork, e.g., a cellular network or a Wi-Fi access point.

In one embodiment, the operation of receiving the data that is capturedby the particular sensor of the one or more sensors is performed in apredetermined time interval. For example, the operation of receiving thedata that is captured by the particular sensor may be performed everyhour, every four hours, etc.

In one embodiment, the receiving of the data that is captured by theparticular sensor of the one or more sensors occurs upon sending a queryfor obtaining the data that is captured by the particular sensor. Thequery may be sent either to the particular sensor or the local gatewayserver that gathers the data that is captured by the particular sensor.

In one embodiment, a time frequency of capturing the data by theparticular sensor of the one or more sensors is determined by thenetwork service provider. In one embodiment, a frequency of receivingthe data that is captured by the particular sensor is determined by thenetwork service provider. For illustrative example, the network serviceprovider may determine that temperature needs to be captured every hour,humidity needs to be captured every four hours, and data that iscaptured by one or more sensors at the location is received every twelvehours via the local gateway server gathering the data at the location,and so on.

In addition to the data that is captured by the one or more sensors, theapplication server also receives atmospheric data for the location atwhich the one or more sensors are deployed. In one embodiment, afrequency of receiving the atmospheric data for the location isdetermined by the network service provider.

In one embodiment, the atmospheric data comprises one or more of: anoutside temperature of the location, an outside humidity level of thelocation, and an outside air quality of the location. In one embodiment,the outside air quality of the location may specify a level ofpollution, a level of a type of gas or chemical, a density of one ormore types of particulates, and the like. For example, the level of gasmay indicate a level of carbon dioxide in the air. In another example,the level of gas may indicate a level of carbon monoxide in the air. Inanother example, the level of gas may indicate a level of chlorine gasin the air. In one embodiment, the one or more types of particulates maycomprise one or more of: dust particulates, pollen particulates, andparticulates defined in terms of their size. For example, the EPA mayspecify an indoor air quality standard that defines a requirement forventilation based on a diameter of a type of particulate. For instance,the requirement for ventilation may be different for particulates withdiameters less than 10 micrometers versus for particulates withdiameters greater than or equal to 10 micrometers. The atmospheric datafor the location that is received may then include the types ofparticulates.

In one embodiment, the atmospheric data is received from a weather datasource, e.g., a database of a national weather service. For example, inthe United States of America, the National Oceanic and AtmosphericAdministration (NOAA) maintains a website (e.g., weather.gov) forenabling users to receive weather information for any location in theUnited States of America. The application server may then receive theatmospheric data for any location in a predetermined interval and storethe atmospheric data in a database of the network service provider. Whenthe atmospheric data is needed for analysis, the atmospheric data maythen readily be retrieved from the database in which it is stored.

In one embodiment, for each equipment of the one or more equipment, theapplication server utilizes an analytical engine for generating abaseline for each particular parameter of the one or more parameters ofthe equipment. The analytical engine may comprise a prediction model.The prediction model is trained using historical (i.e., known)atmospheric and performance data. In other words, from historicalrecords associated with each equipment of the one or more equipment, theprediction model learns the relationship between the atmospheric datafor the location at which the equipment is deployed and the performancedata of the equipment. For example, the prediction model may learnhistorically (e.g., using historical data) that on days with a maximumoutside temperature of greater than or equal to 90° F., the averagepercentage of time a particular HVAC system operates is 60%. Similarly,the predication model may learn historically, on days with a maximumoutside temperature of 70° F.≦maximum outside temperature<90° F., theaverage percentage of time the particular HVAC system operates is 45%.Hence, from the historical records associated with the particular HVACsystem, a baseline for a percentage of time the particular HVAC systemis operating versus a maximum outside temperature (for a day or anyportion of a day) may be generated.

Similarly, a baseline may be generated for all the other parameters ofthe HVAC system. For the example described above, the application servermay generate: a baseline for a percentage of time the condenser isrunning during a 24 hour period versus the maximum outside temperaturethat is observed during the same 24 hour period at the location of theHVAC system, a baseline for an average coolant temperature observedduring a 24 hour period versus the maximum outside temperature that isobserved during the same 24 hour period at the location of the HVACsystem, a baseline for a percentage of time the heat pump is runningduring a 24 hour period versus the minimum outside temperature that isobserved during the same 24 hour period at the location of the HVACsystem, a baseline for an average temperature of a heating element thatis observed during a 24 hour period versus the minimum outsidetemperature that is observed during the same 24 hour period at thelocation of the HVAC system, and the like.

In turn, the application server, for each particular parameter of theequipment, establishes a threshold for a maximum deviation from thebaseline that is generated for the same parameter. For example, for somedeployment scenarios, how far a value of a parameter is from a valuethat is considered normal may be relevant.

For an illustrative example, assume a building has five HVAC systems ofthe same make and model, with one particular HVAC system being locatedin a room with several heat generating devices while the remaining fourHVAC systems are in other rooms that have no heat generating devices.Assume also that the four HVAC systems are running on average 40% of thetime, and the data collected on the percentage of times that the fourHVAC systems are running within a range of 35% and 45%. For theparticular HVAC system that is located in the room with several heatgenerating devices, the data collected on the percentage of time thatthe particular HVAC system is running may indicate that the particularHVAC is running on average 65% of the time, and the data collected onthe percentage of time that the particular HVAC system is running withina range of 50% and 80%. Accordingly, for the particular HVAC system, thebaseline for a percentage of time the particular HVAC system is runningmay be set to 65% and the threshold for the maximum deviation from thebaseline for the percentage of time the particular HVAC is running maybe set such that running the particular HVAC system 80% of the timewould not trigger a scheduling of a maintenance action. In other words,for each particular parameter of the equipment, the baseline isgenerated first. Then, the threshold for the maximum deviation from thebaseline is established in a manner that would not cause triggering of amaintenance action when the equipment is running at a level that isacceptable for the particular deployment scenario of the equipment. Forthe particular HVAC system described above, the threshold for themaximum deviation may be set to cause an action when the HVAC system isrunning more than 80% of the time, e.g., 85% of the time.

The application server then monitors each of the one or more parametersof the equipment and the atmospheric data for the location at which theequipment is operating. In one embodiment, the monitoring of aparticular parameter of the one or more parameters comprises receiving avalue of the particular parameter in a predetermined time interval. Thevalue of each parameter of the one or more parameters that is receivedis processed and stored in a database.

In one embodiment, the monitoring of the particular parameter of the oneor more parameters further comprises aggregating a plurality of valuesof the particular parameter, wherein the plurality of values of theparticular parameter are received over a predetermined time foraggregation. For example, the aggregation of the data may be performedevery four hours, every eight hours, every twelve hours, every day,every week, etc.

In one embodiment, the monitoring of the particular parameter of the oneor more parameters comprises monitoring a parameter associated with abattery life. For example, the monitoring may be for a sensor thatoperates on batteries. Then, the monitoring may be for a particularparameter that tracks a battery life associated with the sensor thatoperates on batteries.

In one embodiment, the monitoring of the particular parameter of the oneor more parameters comprises monitoring a parameter associated withsignal strength of a particular sensor of the one or more sensors. Forexample, the signal strength of the particular sensor may be monitoredwhen the receiving of the data that is captured by the particular sensoris performed without having the data that is captured by the particularsensor traversing a local gateway server deployed at the location.

The application server then determines, for each particular parameter ofthe one or more parameters of the equipment, if a deviation from thebaseline that is greater than the threshold for the maximum deviationfrom the baseline that is established for the particular parameter isdetected. For a particular parameter of the one or more parameters ofthe equipment, if there is a deviation from the baseline but thedeviation from the baseline is less than or equal to the threshold forthe maximum deviation from the baseline that is established for theparticular parameter, there is no need to trigger the scheduling of anaction based on the deviation that is detected. However, for theparticular parameter of the one or more parameters of the equipment, ifthere a deviation from the baseline and the deviation from the baselineis greater than the threshold for the maximum deviation from thebaseline that is established for the particular parameter, the deviationmay be an indication that an potential issue with the equipment isdeveloping. In one example, the detecting of the deviation may be usefulfor one or more of: assessing a risk or likelihood of a pending failureof the equipment, and triggering a scheduling of a maintenance action tobe taken before a failure actually occurs. For instance, it is nowpossible to schedule an on-demand maintenance appointment or visit(e.g., a “just-in-time” appointment) for the equipment such that anissue that is developing with the equipment is addressed prior to anactual failure.

An actual “failure” is deemed to be a condition where the equipment isno longer able to meet the performance parameter set for the equipment.For example, an HVAC system not able to maintain a set temperature of78° F. for a properly sized room or area when the outside temperature is85° F. can be deemed to be an actual failure, whereas an HVAC system notable to maintain a set temperature of 72° F. when the outsidetemperature is 107° F. may not be deemed to be an actual failure.

In one embodiment, for each particular parameter of the one or moreparameters of the equipment, when a deviation from the baseline greaterthan the threshold for the maximum deviation from the baseline that isestablished for the particular parameter is detected, the applicationserver determines whether an update to a Risk Priority Number (RPN)(broadly a risk measure) associated with the equipment is needed. An RPNrefers to a number used for prioritizing work orders and/or fordispatching maintenance personnel. For instance, the RPN may be anynumerical value, e.g., a whole number, selected from one to ten, whereinin one indicates a lowest risk priority number and ten indicates ahighest risk priority number. In one embodiment, the applicable valuesfor the RPN may be determined by the network service provider. It isnoted that the numbers above (i.e., one through ten) are examples andare not intended to be a limitation on the present disclosure.

In one embodiment, for each particular parameter of the one or moreparameters of the equipment, the update to the RPN associated with theequipment is needed when the deviation from the baseline greater thanthe threshold for the maximum deviation from the baseline that isestablished for the particular parameter is sustained for a timeinterval greater than or equal to a predetermined length of time forsuch sustained deviation. If the deviation, described above, issustained for a time interval greater than or equal to the predeterminedlength of time for sustained deviation, then the RPN associated with theequipment may be incremented, e.g., from one to two, and then to three,and so on. For example, if an undesirable situation (e.g., a degradationof performance) with the equipment is progressing in a negative manner(e.g., the condition is getting worse or deteriorating), the RPN may beincremented. The RPN may be incremented until it reaches a maximum valuefor the RPN. In another example, a previously detected undesirablesituation may improve. Accordingly, when an improvement is detected, theRPN may be decremented.

In one embodiment, for each particular parameter of the one or moreparameters of the equipment, if the deviation from the baseline that isgreater than the threshold for the maximum deviation from the baselinethat is established for the particular parameter is not detected for apredetermined time interval for resetting the risk priority numberassociated with the equipment, the risk priority number is reset to alowest value of the risk priority number associated with the equipment.For illustration, suppose the predetermined time interval for resettingthe risk priority number is a week. Then, if no deviation from thebaseline greater than the respective threshold for the maximum deviationfrom the baseline that is established for the particular parameter isdetected for any given week, the risk priority number associated withthe equipment may be reset to its lowest value, e.g., to one.

The application server then determines whether the current value of theRPN for an equipment will trigger a remedial action. For example, theRPN may have reached a threshold for generating a ticket for taking anaction. The application server generates the ticket when the RPNtriggers a remedial action. In one embodiment, the ticket may be fordispatching maintenance personnel. In one embodiment, the ticket may befor making an adjustment to an existing maintenance schedule. Forexample, the existing maintenance schedule may indicate that maintenancepersonnel will be visiting the equipment site in ten days. However, thecurrent value of the RPN may indicate that an issue is developing withthe equipment and remediation within the next three days is moreappropriate. Then, the ticket may be generated to make adjustments to amaintenance schedule and to ensure that a maintenance visit to thelocation of the equipment occurs within the next three days.

In one embodiment, the application server sends the ticket that isgenerated to a system or a user. For example, the user may be anindividual or an organization responsible for maintenance of theequipment or dispatching of maintenance personnel. The applicationserver then continues monitoring each of the one or more parameters ofthe equipment.

FIG. 1 illustrates an example network 100 related to the presentdisclosure. In one illustrative embodiment, the network 100 may comprisea building 111 (broadly any enclosed environment), an access network101, a core network 103, a system 125 (e.g., a server) of an entity thatprovides atmospheric data, a system 126 (e.g., a server) of an entitythat provides data about equipment, components of equipment and/orsensors, a system 127 (e.g., a server) of an agency that providesvarious standards such as indoor air quality and the like, a system 128(e.g., a server) of a user, e.g., a system of a dispatcher ormaintenance personnel, and a database server 129 containing equipmentmaintenance data. For example, records on maintenance of equipment maybe stored in the database 129.

The access network 101 may comprise a Wireless-Fidelity (Wi-Fi) network,a cellular network (e.g., 2G, 3G, and the like), a long term evolution(LTE) network, and the like. The core network 103 may comprise any typeof communications network, such as for example, a traditional circuitswitched network (e.g., a public switched telephone network (PSTN)) or apacket network such as an Internet Protocol (IP) network (e.g., an IPMultimedia Subsystem (IMS) network), an asynchronous transfer mode (ATM)network, or a wireless network. It should be noted that an IP network isbroadly defined as a network that uses Internet Protocol to exchangedata packets.

In one embodiment, the building 111 comprises an equipment 112, one ormore sensor devices 113 a-113 n, and a local gateway server 114. In oneembodiment, the core network 103 may include an Application Server (AS)104 and a database server 106. In one embodiment, the AS 104 isconfigured to perform the methods and functions described herein (e.g.,the method 200 discussed below). For example, the AS 104 may be deployedas a hardware device embodied as a dedicated server (e.g., the dedicatedcomputer 300 as illustrated in FIG. 3). In other words, the AS 104 isfor providing equipment maintenance in accordance with the teachings ofthe present disclosure. The application server 104 may comprise ananalytic engine 105. The application server 104 may be communicativelycoupled with the database server 106.

In one embodiment, the database server 106 may be used for storing datagathered from various internal and external sources. For example,atmospheric data gathered from the system 125, equipment and sensor datagathered from the system 126, standards for indoor air quality gatheredfrom the system 127, and maintenance data gathered from the database129, may be stored in the database server 106. The application server104 may then access the data gathered from the various internal andexternal sources when performing an analysis, generating a baseline, andproviding the equipment maintenance.

In one embodiment, the one or more sensor devices 113 a-113 n maycommunicate with the application server 104 via the local gateway server114 and the access network 101. In one embodiment, the one or moresensor devices 113 a-113 n may communicate with the application server104 directly via an access network, e.g., via a cellular network or aWi-Fi access point. A sensor device is said to be communicating directlyvia an access network when the communication occurs without the use ofthe local gateway server, e.g., the local gateway server 114.

It should be noted that the network 100 may include additional networksand/or elements that are not shown to simplify FIG. 1. For example, theaccess network and the core network of FIG. 1 may include additionalnetwork elements (not shown), such as for example, base stations, borderelements, gateways, firewalls, routers, switches, call control elements,various application servers, and the like. In addition, the building 111may include any number of equipment (e.g., HVAC systems), sensors, localgateway servers, etc.

Although a single database is shown in core network 103 of FIG. 1, thevarious types of data may be stored in any number of databases. Forinstance, various databases, e.g., a database for equipment, a databasefor sensors, a database for standards, a database for guidance forindoor air quality, a database for atmospheric data, a database forbuilding and equipment maintenance data, a database for battery life ofsensors, etc., may be used. In addition, the various types of data mayalso be stored in a cloud storage. In other words, the network serviceprovider may implement the service for providing equipment maintenanceof the present disclosure by utilizing distributed sensor devices andstoring data in a cloud storage and/or a centralized server.

In one embodiment, the AS 104 is used for implementing the presentmethod for providing equipment maintenance. The AS 104 of the presentdisclosure is for receiving, for each particular sensor of one or moresensors at a location, data that is captured by the particular sensor,wherein the data that is captured by the particular sensor is associatedwith at least one parameter of one or more parameters of an equipment,wherein the equipment is deployed at the location, for receivingatmospheric data for the location, for monitoring each particularparameter of the one or more parameters of the equipment and theatmospheric data for the location, for determining, for each particularparameter of the equipment, if an update to a risk priority numberassociated with the equipment is needed, when a deviation from abaseline greater than a threshold for a maximum deviation from thebaseline that is established for a particular parameter is detected, forperforming the update to the risk priority number associated with theequipment, when the update to the risk priority number associated withthe equipment is needed, and for generating a ticket for a remedialaction based on the risk priority number associated with the equipment,when the risk priority number associated with the equipment causes aremedial action.

FIG. 2 illustrates a flowchart of an example method 200 for providingequipment maintenance in accordance with the present disclosure. In oneembodiment, the method 200 may be implemented in an application server,e.g., an application server 104, or the processor 302 as described inFIG. 3.

The method 200 may be implemented for any number of locations and anynumber of equipment at each location. For example, the AS 104 may beused for a plurality of locations of an enterprise, with any number ofequipment being maintained via the AS 104 at each location of theenterprise. For clarity, the flowchart of the example method 200 isdescribed herein for each equipment. However, the method may beperformed for any number of equipment in parallel. The method 200 startsin step 205 and proceeds to step 207.

In optional step 207, the processor receives, for a location,information about equipment to be maintained and one or more sensorsthat are deployed at the location. For example, for the location, theprocessor may receive, a physical location of each equipment and eachsensor, a lead unit designation (if applicable), a type of communicationfor each sensor of the one or more sensors, etc.

In step 210, the processor receives, for each particular sensor of theone or more sensors at the location, data that is captured by theparticular sensor of the one or more sensors, wherein the data that iscaptured by the particular sensor of the one or more sensors isassociated with at least one parameter of one or more parameters of anequipment, wherein the equipment is deployed at the location.

In one embodiment, each particular sensor of the one or more sensors isfor capturing the data that is received from the particular sensor,wherein the data that is captured by the particular sensor is associatedwith at least one parameter of the one or more parameters of theequipment.

In one embodiment, each particular parameter of the one or moreparameters of the equipment is a parameter that is defined for trackingone or more measurable aspects of the equipment. In one embodiment, ameasurable aspect of the one or more measurable aspects of the equipmentmay be for tracking at least one of: a percentage of time the equipmentis running, a percentage of time a component of the equipment isrunning, a temperature of the location at which the equipment isdeployed, and a temperature of a component of the equipment.

In step 215, the processor receives atmospheric data for the location.For example, the processor receives atmospheric data from a weather datasource, e.g., a database of a national weather service.

In optional step 220, the processor generates a baseline for eachparticular parameter of the one or more parameters of the equipment.

In optional step 225, the processor, for each particular parameter ofthe one or more parameters of the equipment, establishes a threshold fora maximum deviation from the baseline that is generated for theparticular parameter of the one or more parameters of the equipment. Forexample, if there are five HVAC systems in a building and threeparameters are to be monitored for each HVAC system, fifteen baselinesare generated, one baseline for a particular parameter of a particularHVAC system. Then, for each of the fifteen baselines, a threshold for amaximum deviation from the baseline (i.e., the baseline of therespective parameter) is established.

In step 230, the processor monitors each particular parameter of the oneor more parameters of the equipment and the atmospheric data for thelocation. For example, the processor may monitor a temperature, ahumidity level, and so on for the location. For the example above, eachof the fifteen particular parameters at the location is monitored. Inaddition, the atmospheric data for the location at which the five HVACsystems are deployed is monitored.

In step 235, the processor determines, for each particular parameter ofthe one or more parameters of the equipment, whether a deviation fromthe baseline greater than the threshold for the maximum deviation fromthe baseline that is established for the particular parameter isdetected. If the deviation from the baseline greater than the thresholdfor the maximum deviation that is established for the particularparameter is detected, the processor proceeds to step 250. Otherwise,the processor proceeds to step 230.

In step 250, the processor determines whether an update to a riskpriority number associated with the equipment is needed. If theprocessor determines an update to the risk priority number associatedwith the equipment is needed, the processor proceeds to step 255.Otherwise, the processor proceeds to step 230.

In step 255, the processor performs the update to the risk prioritynumber associated with the equipment. For example, the risk prioritynumber associated with the equipment may be incremented when a situationat the location deteriorates or progresses negatively. Similarly, whenan improvement in a previously known situation is detected, the riskpriority number associated with the equipment may be decremented.

In step 260, the processor determines whether the risk priority numberassociated with the equipment causes or triggers a remedial action to betaken. For example, the risk priority number associated with theequipment may have reached a threshold for generating a ticket, e.g., amaintenance ticket. If the risk priority number associated with theequipment causes the remedial action, the processor proceeds to step265. Otherwise, the processor proceeds to step 230.

In step 265, the processor generates a ticket for the remedial action tobe performed. The ticket is based on the risk priority number associatedwith the equipment. In one embodiment, the processor generates theticket for dispatching maintenance personnel to the location of theequipment. In one embodiment, the processor generates the ticket forupdating a maintenance schedule. For example, the updating of themaintenance schedule may be for performing maintenance at an earliertime than previously scheduled. In another example, the updating of themaintenance schedule may be for performing maintenance at a later timethan previously scheduled.

In optional step 270, the processor sends the ticket that is generatedto a user or a system. For example, the ticket may be sent to anindividual or a system responsible for maintenance of the equipment atthe location or dispatching of maintenance personnel to the location.The processor then proceeds either to step 207 to receive information,to step 230 to continue monitoring, or to step 299 to end the process.

It is important to note that the equipment (types and quantities), themeasurable aspects, types of sensors, the types of measurements that areto be performed, the types of computations that are performed, thelength of time used for comparisons, etc. are not intended to limit theapplicability of the teachings of the present disclosure. For example,the local gateway server may comprise a 900 MHz 3G cellular networkgateway server or any other gateway server. The sensors may comprise atemperature sensor (e.g., one or more sensors for measuring air or spacetemperature, for measuring discharge line temperature, for measuring airsupply line temperature, for measuring suction line temperature, formeasuring discharged condenser air temperature and the like), a humiditysensor, a timer for run times, a discharge sensor (e.g., for air, forfluid, and the like), etc. The measurements may be absolutemeasurements, or measurements of differences between predicted andactual values obtained via sensors. In addition, the computed values(e.g., the RPN) may be modified for ease in understanding and/or changea scale of results. For instance, a smoothing function may be applied oncomputed values for providing results within a given range. For example,the RPN may be provided in a range of one to ten, a geometric weight maybe applied to take into account a deviation being sustained for a longertime, etc.

In addition, although not specifically specified, one or more steps,functions or operations of method 200 may include a storing, displayingand/or outputting step as required for a particular application. Inother words, any data, records, fields, and/or intermediate resultsdiscussed in the method can be stored, displayed and/or outputted eitheron the device executing the method or to another device, as required fora particular application.

Furthermore, steps, blocks, functions or operations in FIG. 2 thatrecite a determining operation or involve a decision do not necessarilyrequire that both branches of the determining operation be practiced. Inother words, one of the branches of the determining operation can bedeemed as an optional step. Moreover, steps, blocks, functions oroperations of the above described method 200 can be combined, separated,and/or performed in a different order from that described above, withoutdeparting from the example embodiments of the present disclosure.

As such, the present disclosure provides at least one advancement in thetechnical field of equipment maintenance. For instance, in one example,the present disclosure provides a server and a communication networkthat is able to analyze data collected from various sources to identifypredict equipment issues and generate a ticket, e.g., for a dispatch ofpersonnel for a preventative action or for adjusting a schedule of apreviously scheduled maintenance of the equipment.

Although the above disclosure was discussed in the context of an HVACsystem, other types of equipment deployed in a building can also benefitfrom the monitoring methods of the present disclosure. For example,sophisticated medical equipment or laboratory equipment used in ahospital or a laboratory can also use the monitoring methods of thepresent disclosure.

FIG. 3 depicts a high-level block diagram of a computer suitable for usein performing the functions described herein. As depicted in FIG. 3, thesystem 300 comprises one or more hardware processor elements 302 (e.g.,a central processing unit (CPU), a microprocessor, or a multi-coreprocessor), a memory 304, e.g., random access memory (RAM) and/or readonly memory (ROM), a module 305 for providing equipment maintenance, andvarious input/output devices 306 (e.g., storage devices, including butnot limited to, a tape drive, a floppy drive, a hard disk drive or acompact disk drive, a receiver, a transmitter, a speaker, a display, aspeech synthesizer, an output port, an input port and a user inputdevice (such as a keyboard, a keypad, a mouse, a microphone and thelike)). Although only one processor element is shown, it should be notedthat the computer may employ a plurality of processor elements.Furthermore, although only one computer is shown in the figure, if themethod 200 as discussed above is implemented in a distributed orparallel manner for a particular illustrative example, i.e., the stepsof the above method 200, or each of the entire method 200 is implementedacross multiple or parallel computers, then the computer of this figureis intended to represent each of those multiple computers.

Furthermore, one or more hardware processors can be utilized insupporting a virtualized or shared computing environment. Thevirtualized computing environment may support one or more virtualmachines representing computers, servers, or other computing devices. Insuch virtualized virtual machines, hardware components such as hardwareprocessors and computer-readable storage devices may be virtualized orlogically represented.

It should be noted that the present disclosure can be implemented insoftware and/or in a combination of software and hardware, e.g., usingapplication specific integrated circuits (ASIC), a programmable gatearray (PGA) including a Field PGA, or a state machine deployed on ahardware device, a computer or any other hardware equivalents, e.g.,computer readable instructions pertaining to the method(s) discussedabove can be used to configure a hardware processor to perform thesteps, functions and/or operations of the above disclosed method.

In one embodiment, instructions and data for the present module orprocess 305 for providing equipment maintenance (e.g., a softwareprogram comprising computer-executable instructions) can be loaded intomemory 304 and executed by hardware processor element 302 to implementthe steps, functions or operations as discussed above in connection withthe illustrative method 200. Furthermore, when a hardware processorexecutes instructions to perform “operations,” this could include thehardware processor performing the operations directly and/orfacilitating, directing, or cooperating with another hardware device orcomponent (e.g., a co-processor and the like) to perform the operations.

The processor executing the computer readable or software instructionsrelating to the above described method can be perceived as a programmedprocessor or a specialized processor. As such, the present module 305for providing equipment maintenance (including associated datastructures) of the present disclosure can be stored on a tangible orphysical (broadly non-transitory) computer-readable storage device ormedium, e.g., volatile memory, non-volatile memory, ROM memory, RAMmemory, magnetic or optical drive, device or diskette and the like.Furthermore, a “tangible” computer-readable storage device or mediumcomprises a physical device, a hardware device, or a device that isdiscernible by the touch. More specifically, the computer-readablestorage device may comprise any physical devices that provide theability to store information such as data and/or instructions to beaccessed by a processor or a computing device such as a computer or anapplication server.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and nota limitation. Thus, the breadth and scope of a preferred embodimentshould not be limited by any of the above-described exemplaryembodiments, but should be defined only in accordance with the followingclaims and their equivalents.

What is claimed is:
 1. A method comprising: receiving, via a processorof a communications network operated by a network service provider, datathat is captured by at least one sensor deployed at a location, whereinthe data is associated with at least one parameter of an equipment,wherein the equipment is deployed at the location; receiving, via theprocessor, atmospheric data for the location; monitoring, via theprocessor, the at least one parameter of the equipment and theatmospheric data for the location; determining, via the processor, forthe at least one parameter of the equipment, whether an update to a riskmeasure associated with the equipment is needed, when a deviation from abaseline is detected that is greater than a threshold for a maximumdeviation from the baseline that is established for the at least oneparameter; performing, via the processor, the update to the risk measureassociated with the equipment, when the update to the risk measureassociated with the equipment is needed; and generating, via theprocessor, a ticket for a remedial action based on the risk measureassociated with the equipment, when the risk measure associated with theequipment triggers the scheduling of the remedial action.
 2. The methodof claim 1, further comprising: generating, via the processor, thebaseline for the at least one parameter; and establishing, via theprocessor, the threshold for the maximum deviation from the baseline forthe at least one parameter of the equipment.
 3. The method of claim 1,further comprising: sending, via the processor, the ticket that isgenerated to a system for scheduling the remedial action.
 4. The methodof claim 1, wherein the generating the ticket is for dispatchingmaintenance personnel to the location.
 5. The method of claim 1, whereinthe generating the ticket is for updating a maintenance schedule for thelocation.
 6. The method of claim 1, wherein the at least one parameteris a parameter that is defined for tracking one or more measurableaspects of the equipment.
 7. The method of claim 6, wherein a measurableaspect of the one or more measurable aspects of the equipment is fortracking at least one of: a percentage of time the equipment isoperating, a percentage of time a component of the equipment isoperating, a temperature of the location at which the equipment isdeployed, and a temperature of a component of the equipment.
 8. Themethod of claim 1, further comprising: receiving, via the processor,information about the equipment and the at least one sensor deployed atthe location.
 9. The method of claim 1, wherein the data is receivedfrom a local gateway server deployed at the location.
 10. The method ofclaim 1, wherein the data is received directly from the at least onesensor without the data traversing over a local gateway server deployedat the location.
 11. The method of claim 1, wherein the data is receivedin a predetermined time interval.
 12. The method of claim 1, wherein thedata is received in response to a query directed to the at least onesensor.
 13. The method of claim 1, wherein a frequency of receiving thedata is determined by the network service provider.
 14. The method ofclaim 1, wherein a frequency of capturing the data by the at least onesensor is determined by the network service provider.
 15. The method ofclaim 1, wherein the atmospheric data comprises one or more of: anoutside temperature of the location, an outside humidity level of thelocation, and an outside air quality of the location.
 16. The method ofclaim 1, wherein the monitoring of the at least one parameter comprises:receiving, via the processor, a value of the at least one parameter in apredetermined time interval.
 17. The method of claim 16, wherein themonitoring of the at least one parameter further comprises: aggregating,via the processor, a plurality of values of the at least one parameter,wherein the plurality of values of the at least one parameter isreceived over a predetermined time for aggregation.
 18. The method ofclaim 1, wherein the monitoring of the at least one parameter comprises:monitoring, via the processor, a parameter associated with a batterylife.
 19. A non-transitory computer-readable storage device storing aplurality of instructions which, when executed by a processor of acommunications network operated by a network service provider, cause theprocessor to perform operations, the operations comprising: receivingdata that is captured by at least one sensor deployed at a location,wherein the data is associated with at least one parameter of anequipment, wherein the equipment is deployed at the location; receivingatmospheric data for the location; monitoring the at least one parameterof the equipment and the atmospheric data for the location; determiningfor the at least one parameter of the equipment, whether an update to arisk measure associated with the equipment is needed, when a deviationfrom a baseline is detected that is greater than a threshold for amaximum deviation from the baseline that is established for the at leastone parameter; performing the update to the risk measure associated withthe equipment, when the update to the risk measure associated with theequipment is needed; and generating a ticket for a remedial action basedon the risk measure associated with the equipment, when the risk measureassociated with the equipment triggers the scheduling of the remedialaction.
 20. An apparatus comprising: a processor of a communicationsnetwork operated by a network service provider; and a computer-readablestorage device storing a plurality of instructions which, when executedby the processor, cause the processor to perform operations, theoperations comprising: receiving data that is captured by at least onesensor deployed at a location, wherein the data is associated with atleast one parameter of an equipment, wherein the equipment is deployedat the location; receiving atmospheric data for the location; monitoringthe at least one parameter of the equipment and the atmospheric data forthe location; determining for the at least one parameter of theequipment, whether an update to a risk measure associated with theequipment is needed, when a deviation from a baseline is detected thatis greater than a threshold for a maximum deviation from the baselinethat is established for the at least one parameter; performing theupdate to the risk measure associated with the equipment, when theupdate to the risk measure associated with the equipment is needed; andgenerating a ticket for a remedial action based on the risk measureassociated with the equipment, when the risk measure associated with theequipment triggers the scheduling of the remedial action.